{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc7c5bff",
   "metadata": {},
   "source": [
    "Python notebook for reproducing Figure S1 of M. J. Jones et al., \"Can the Moon's center of mass–center of figure offset be explained with a uniform primordial crust?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66341cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "### //Imports//\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as mticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04f20262",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['font.family'] = 'Myriad Pro'\n",
    "mpl.rcParams['figure.dpi'] = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33c0f274",
   "metadata": {},
   "outputs": [],
   "source": [
    "### //Function definitions//\n",
    "def temps(zs, grad):\n",
    "    Tsurf = 250\n",
    "    Ts = np.zeros(np.shape(zs))\n",
    "    Ts = Tsurf + zs/1e3*grad\n",
    "    return Ts\n",
    "\n",
    "def viscosity(Ts):\n",
    "    n0 = 1e18 #Pa s\n",
    "    Q = 4e5 #J/mol -- for anorthite/anorthosite (Dimanov et al., 2000)\n",
    "    R = 8.3145\n",
    "    return n0*np.exp(Q/(R*Ts) - Q/(R*1513))\n",
    "\n",
    "def phi(phi0, zs, grad, ts):\n",
    "    Ts = temps(zs, grad)\n",
    "    rho = 2900 #[kg/m^3] crust density (no porosity) from Wieczorek+ 2013\n",
    "    n = viscosity(Ts)\n",
    "\n",
    "    tau = n / (rho*g*zs)\n",
    "    vals = []\n",
    "    for t in ts:\n",
    "        vals.append(phi0*np.exp(-t/tau))\n",
    "    return np.asarray(vals)\n",
    "  \n",
    "def radial_change(model, zs):\n",
    "    phi0 = model[0,0] #assuming the model starts at time zero\n",
    "    thick = np.nanmax(zs)\n",
    "    try:\n",
    "        transition = zs[np.nanmin(np.where(model[-1,:] < 0.01))] #below this porosity is removed\n",
    "\n",
    "        r = np.cbrt((R-transition)**3 - (1-phi0)*((R-transition)**3 - (R-thick)**3))\n",
    "\n",
    "        return (thick/1e3 - (R-r)/1e3), (transition/1e3)\n",
    "    except ValueError:\n",
    "        return 0, np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9729e6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### //Physical constants//\n",
    "k0 = 2 #W/m K -- thermal conductivity of anorthosite\n",
    "G = 6.67e-11 #m^3/kg s^2 -- Big G\n",
    "R = 1737e3 #km -- radius of the Moon\n",
    "M = 3346*4/3*np.pi*1737e3**3 #kg -- mass of the Moon\n",
    "g = G*M / R**2 #m/s^2 -- Moon surface gravity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0439ecf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dphi():\n",
    "  def __init__(self, dTdzs, hcs, phi_start, tmax_Myr=20, n_ts=200):\n",
    "    self.dTdz = dTdzs\n",
    "    self.hc = hcs\n",
    "    self.phi0 = phi_start\n",
    "    self.tmax = tmax_Myr\n",
    "    self.nt = n_ts\n",
    "    \n",
    "    return\n",
    "  \n",
    "  \n",
    "  \n",
    "  def plot_results_time_series(self, tmax=None, plot_ztrans=False, line_cmap=None):\n",
    "    # Set up time\n",
    "    if tmax is None: tmax = self.tmax\n",
    "    ts = np.linspace(0, tmax*3.154e13, num=self.nt) #s\n",
    "    tplot = ts/3.154e13 #Myr\n",
    "    \n",
    "    # Set up subplots w/ or w/o space for z_trans\n",
    "    if plot_ztrans: fig, ax = plt.subplots(1, 3, figsize=(30,6))\n",
    "    else: fig, ax = plt.subplots(1, 2, figsize=(20,6))\n",
    "    \n",
    "    # Set plot parameters\n",
    "    if line_cmap is None:\n",
    "      colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "    else:\n",
    "      line_cmap = mpl.cm.get_cmap(line_cmap)\n",
    "      colors = [line_cmap(x) for x in np.linspace(0, 0.9, len(self.dTdz))]\n",
    "    styles = ['-', '--', '-.', ':']\n",
    "    \n",
    "    lw = 2.5\n",
    "    \n",
    "    # Compute and plot results\n",
    "    for ii, dT in enumerate(self.dTdz):\n",
    "      # Set line color\n",
    "      cidx = ii % len(colors)\n",
    "      c = colors[cidx]\n",
    "      \n",
    "      # Reset legend parameter\n",
    "      labeled = False\n",
    "      \n",
    "      for jj, hc in enumerate(self.hc):\n",
    "        # Set line style\n",
    "        stidx = jj % len(styles)\n",
    "        st = styles[stidx]\n",
    "        \n",
    "        # Set up depth\n",
    "        zs = np.linspace(0, hc, 1000) * 1e3\n",
    "        \n",
    "        # Solve for porosity evolution\n",
    "        phis = phi(self.phi0, zs, dT, ts)\n",
    "\n",
    "        # Take average porosity w/ respect to time\n",
    "        phis_avg = np.average(phis.T, weights=((1-phis.T)/(1-self.phi0)), axis=0)\n",
    "        \n",
    "        # Compute Δhc (`Dz`) and z_trans w/ respect to time\n",
    "        Dzs = []\n",
    "        zs_trans = []\n",
    "        for tt in range(phis.shape[0]):\n",
    "          phi_t = np.reshape(phis[tt], (1, -1))\n",
    "          [Dz, z_trans] = radial_change(phi_t, zs)\n",
    "          Dzs.append(Dz)\n",
    "          zs_trans.append(z_trans)\n",
    "        \n",
    "        # Skip scenarios w/ final results outside of constraints\n",
    "        if (Dzs[-1] > 43) or (phis_avg[-1] > 0.15):\n",
    "          continue\n",
    "        \n",
    "        # Define legend label\n",
    "        if labeled:\n",
    "          lbl = '_nolegend_'\n",
    "        else:\n",
    "          lbl = r'$\\frac{dT}{dz}=$' + f'{dT:.0f} K/km'\n",
    "          labeled = True\n",
    "        \n",
    "        # Plot results\n",
    "        ax[0].plot(tplot, Dzs, color=c, linestyle=st, linewidth=lw, label=lbl)\n",
    "        \n",
    "        if plot_ztrans:\n",
    "          ax[1].plot(tplot, zs_trans, color=c, linestyle=st, linewidth=lw, label=lbl)\n",
    "\n",
    "        ax[-1].plot(tplot, phis_avg, color=c, linestyle=st, linewidth=lw, label=lbl)\n",
    "      \n",
    "    # Format plots\n",
    "    ax[0].set_ylabel(r'$\\Delta h_{c}$ (km)')\n",
    "    ax[0].set_ylim((0, 8))\n",
    "    \n",
    "    if plot_ztrans:\n",
    "      ax[1].set_ylabel('Transition depth (km)')\n",
    "      ax[1].set_ylim((np.nanmax(self.hc), 0))\n",
    "    \n",
    "    ax[-1].set_ylabel('Average porosity (vol. %)')\n",
    "    ax[-1].set_ylim((0, 0.3))\n",
    "    ax[-1].yaxis.set_major_formatter(mticker.PercentFormatter(1.0, decimals=0))\n",
    "    \n",
    "    for a in ax:\n",
    "      a.set_xlabel('Time (Myr)')\n",
    "      a.set_xlim((ts.min()/3.154e13, ts.max()/3.154e13))\n",
    "      a.grid()\n",
    "      a.legend()\n",
    "    \n",
    "    fig.suptitle(r'$\\phi_{0}=$' + f'{self.phi0:.0%}')\n",
    "    \n",
    "    return\n",
    "  \n",
    "  \n",
    "  \n",
    "def plot_results_time_series_all(obj_list, tmax=None, plot_ztrans=False, line_cmap=None):\n",
    "  # Set up time\n",
    "  if tmax is None:\n",
    "    tmax = obj_list[0].tmax\n",
    "  ts = np.linspace(0, tmax*3.154e13, num=obj_list[0].nt) #s\n",
    "  tplot = ts/3.154e13 #Myr\n",
    "\n",
    "  # Set up subplots w/ or w/o space for z_trans\n",
    "  ncol = len(obj_list)\n",
    "  \n",
    "  if plot_ztrans:\n",
    "    nrow = 3\n",
    "  else:\n",
    "    nrow = 2\n",
    "    \n",
    "  fig, ax = plt.subplots(nrow, ncol, figsize=(12*ncol, 10*nrow), sharex=True, sharey='row', \\\n",
    "                         gridspec_kw={'wspace': .12, 'hspace': .12})\n",
    "\n",
    "  # Define plot parameters\n",
    "  if line_cmap is None:\n",
    "    colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "  else:\n",
    "    line_cmap = mpl.cm.get_cmap(line_cmap)\n",
    "    colors = [line_cmap(x) for x in np.linspace(0, 0.8, len(obj_list[0].dTdz))]\n",
    "  \n",
    "  styles = ['-', '--', '-.', ':']\n",
    "\n",
    "  lw = 3.5\n",
    "\n",
    "  # Compute and plot results\n",
    "  for kk, scn in enumerate(obj_list):\n",
    "    # Define current working list of axes\n",
    "    a = ax[:, kk]\n",
    "    \n",
    "    # Reset legend lists list\n",
    "    h = []\n",
    "    lbls = []\n",
    "    for ii, dT in enumerate(scn.dTdz):\n",
    "      \n",
    "      # Set line color\n",
    "      cidx = ii % len(colors)\n",
    "      c = colors[cidx]\n",
    "\n",
    "      for jj, hc in enumerate(scn.hc):\n",
    "        # Set line style\n",
    "        stidx = jj % len(styles)\n",
    "        st = styles[stidx]\n",
    "\n",
    "        # Set up depth\n",
    "        zs = np.linspace(0, hc, 1000) * 1e3\n",
    "\n",
    "        # Solve for porosity evolution\n",
    "        phis = phi(scn.phi0, zs, dT, ts)\n",
    "\n",
    "        # Take average porosity w/ respect to time\n",
    "        phis_avg = np.average(phis.T, weights=((1-phis.T)/(1-scn.phi0)), axis=0)\n",
    "        \n",
    "        # Skip scenario if porosity didn't evolve\n",
    "        if phis_avg[-1] == scn.phi0:\n",
    "          continue\n",
    "\n",
    "        # Compute Δhc (`Dz`) and z_trans w/ respect to time\n",
    "        Dzs = []\n",
    "        zs_trans = []\n",
    "        for tt in range(phis.shape[0]):\n",
    "          phi_t = np.reshape(phis[tt], (1, -1))\n",
    "          [Dz, z_trans] = radial_change(phi_t, zs)\n",
    "          Dzs.append(Dz)\n",
    "          zs_trans.append(z_trans)\n",
    "\n",
    "        # Define legend label\n",
    "        lbls.append(r\"$\\frac{dT}{dz}=$\" + f\"{dT:.0f} K/km | \" + r\"$h_{c_{0}}=$\" + f\"{hc:.0f} km\")\n",
    "        \n",
    "        # Plot results\n",
    "        a[0].plot(tplot, Dzs, color=c, linestyle=st, linewidth=lw)\n",
    "\n",
    "        if plot_ztrans:\n",
    "          a[1].plot(tplot, zs_trans, color=c, linestyle=st, linewidth=lw)\n",
    "\n",
    "        h.append(a[-1].plot(tplot, phis_avg, color=c, linestyle=st, linewidth=lw)[0])\n",
    "\n",
    "    # Format plots\n",
    "    if kk == 0: a[0].set_ylabel(r'$\\Delta h_{c}$ (km)')\n",
    "    a[0].set_ylim((0, 5))\n",
    "    a[0].set_title(r'$\\phi_{c_{0}}=$' + f'{scn.phi0:.0%}')\n",
    "\n",
    "    if plot_ztrans:\n",
    "      if kk == 0: a[1].set_ylabel(r'$z_{trans}$ (km)')\n",
    "      a[1].set_ylim((np.nanmax(scn.hc), 0))\n",
    "\n",
    "    if kk == 0: a[-1].set_ylabel(r'$\\overline{\\phi}_{c}$ (vol. %)')\n",
    "    a[-1].set_ylim((0, 0.2))\n",
    "    a[-1].yaxis.set_major_formatter(mticker.PercentFormatter(1.0, decimals=0))\n",
    "    a[-1].set_xlabel('Time (Myr)')\n",
    "\n",
    "    for aa in a:\n",
    "      aa.set_xlim((ts.min()/3.154e13, ts.max()/3.154e13))\n",
    "      aa.grid()\n",
    "\n",
    "  fig.legend(h, lbls, loc=(.005, .005), ncols=len(obj_list[0].dTdz))\n",
    "\n",
    "  return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cef2b6d",
   "metadata": {},
   "source": [
    "### Plot parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5984ba2d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3x/ypd1ylcx21x4tjydvxbz09zh0000gq/T/ipykernel_47706/3185389040.py:130: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n",
      "  line_cmap = mpl.cm.get_cmap(line_cmap)\n",
      "/var/folders/3x/ypd1ylcx21x4tjydvxbz09zh0000gq/T/ipykernel_47706/2423894674.py:19: RuntimeWarning: divide by zero encountered in divide\n",
      "  tau = n / (rho*g*zs)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 7200x9000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### //Thermal model analyses//\n",
    "# Initial conditions\n",
    "dTdz = [30, 40, 50] #K/km\n",
    "hc = [30, 38] #km\n",
    "tmax = 20 #Myr\n",
    "\n",
    "# Plot parameters etc.\n",
    "nt = 200\n",
    "cmap = 'rainbow'\n",
    "\n",
    "plt.rcParams.update({'font.size': 32})\n",
    "\n",
    "# Create objects and generate data\n",
    "dphi20 = Dphi(dTdz, hc, phi_start=0.2, tmax_Myr=tmax, n_ts=nt)\n",
    "dphi12 = Dphi(dTdz, hc, phi_start=0.12, tmax_Myr=tmax, n_ts=nt)\n",
    "\n",
    "# Plot results summary\n",
    "plot_results_time_series_all([dphi20, dphi12], plot_ztrans=True, line_cmap=cmap)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
